High Magnification and Long Distance Face Recognition: Database Acquisition, Evaluation, and Enhancement

2006 ◽  
Author(s):  
Yi Yao ◽  
Besma Abidi ◽  
Nathan D. Kalka ◽  
Natalia Schmid ◽  
Mongi Abidi
Keyword(s):  
Face Recognition ◽  
High Magnification ◽  
Long Distance

scholarly journals Homomorphic Filtering and Phase-Based Matching for Cross-Spectral Cross-Distance Face Recognition

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4575
Author(s):  
Fitri Arnia ◽  
Maulisa Oktiana ◽  
Khairun Saddami ◽  
Khairul Munadi ◽  
Roslidar Roslidar ◽  
...  
Keyword(s):  
Face Recognition ◽  
Near Infrared ◽  
Recognition Performance ◽  
Extraction Process ◽  
Face Verification ◽  
Surveillance Systems ◽  
Long Distance ◽  
Face Images ◽  
Homomorphic Filtering ◽  
Band Limited

Facial recognition has a significant application for security, especially in surveillance technologies. In surveillance systems, recognizing faces captured far away from the camera under various lighting conditions, such as in the daytime and nighttime, is a challenging task. A system capable of recognizing face images in both daytime and nighttime and at various distances is called Cross-Spectral Cross Distance (CSCD) face recognition. In this paper, we proposed a phase-based CSCD face recognition approach. We employed Homomorphic filtering as photometric normalization and Band Limited Phase Only Correlation (BLPOC) for image matching. Different from the state-of-the-art methods, we directly utilized the phase component from an image, without the need for a feature extraction process. The experiment was conducted using the Long-Distance Heterogeneous Face Database (LDHF-DB). The proposed method was evaluated in three scenarios: (i) cross-spectral face verification at 1m, (ii) cross-spectral face verification at 60m, and (iii) cross-spectral face verification where the probe images (near-infrared (NIR) face images) were captured at 1m and the gallery data (face images) was captured at 60 m. The proposed CSCD method resulted in the best recognition performance among the CSCD baseline approaches, with an Equal Error Rate (EER) of 5.34% and a Genuine Acceptance Rate (GAR) of 93%.

A Framework for Long Distance Face Recognition Using Dense - and Sparse-Stereo Reconstruction

2009 ◽  
pp. 774-783 ◽  
Author(s):  
Ham Rara ◽  
Shireen Elhabian ◽  
Asem Ali ◽  
Travis Gault ◽  
Mike Miller ◽  
...  
Keyword(s):  
Face Recognition ◽  
Long Distance ◽  
Stereo Reconstruction

scholarly journals Face and Body-Based Human Recognition by GAN-Based Blur Restoration

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5229
Author(s):  
Ja Hyung Koo ◽  
Se Woon Cho ◽  
Na Rae Baek ◽  
Kang Ryoung Park
Keyword(s):  
Face Recognition ◽  
Motion Blur ◽  
Indoor Environments ◽  
Body Images ◽  
Long Distance ◽  
Generative Adversarial Network ◽  
Human Recognition ◽  
Adversarial Network ◽  
Face Images ◽  
Matching Score

The long-distance recognition methods in indoor environments are commonly divided into two categories, namely face recognition and face and body recognition. Cameras are typically installed on ceilings for face recognition. Hence, it is difficult to obtain a front image of an individual. Therefore, in many studies, the face and body information of an individual are combined. However, the distance between the camera and an individual is closer in indoor environments than that in outdoor environments. Therefore, face information is distorted due to motion blur. Several studies have examined deblurring of face images. However, there is a paucity of studies on deblurring of body images. To tackle the blur problem, a recognition method is proposed wherein the blur of body and face images is restored using a generative adversarial network (GAN), and the features of face and body obtained using a deep convolutional neural network (CNN) are used to fuse the matching score. The database developed by us, Dongguk face and body dataset version 2 (DFB-DB2) and ChokePoint dataset, which is an open dataset, were used in this study. The equal error rate (EER) of human recognition in DFB-DB2 and ChokePoint dataset was 7.694% and 5.069%, respectively. The proposed method exhibited better results than the state-of-art methods.

Low-resolution degradation face recognition over long distance based on CCA

2015 ◽  
Vol 26 (7) ◽  
pp. 1645-1652 ◽  
Author(s):  
Zhenyu Wang ◽  
Wankou Yang ◽  
Xianye Ben
Keyword(s):  
Face Recognition ◽  
Low Resolution ◽  
Long Distance
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